Data de-identification with minimal data change operations to maintain privacy and data utility

ABSTRACT

Methods, systems, and computer program products are provided for producing de-identified data from a dataset. A first set of constraints are determined based on anonymity requirements from a privacy model. A second set of constraints are determined based on knowledge hiding requirements. A model is generated to determine minimum loss of analytic utility in the dataset for de-identification while satisfying the first set of constraints and the second set of constraints. The model is applied to the dataset to determine changes to the dataset for de-identification that satisfy the first set of constraints and the second set of constraints. De-identified data is produced by modifying the dataset in accordance with the determined changes.

BACKGROUND 1. Technical Field

Present invention embodiments relate to methods, systems and computerprogram products for de-identification of a dataset. In particular,present invention embodiments relate to production of a new dataset byminimally distorting a dataset such that all anonymity constraints aresatisfied and all non-conflicting sensitive knowledge hiding constraintsare satisfied.

2. Discussion of the Related Art

Data anonymization, also known as data sanitization, is a process forprotecting personally identifiable information, including both directidentifiers such as, for example, social security numbers, customernumbers, persons' full names, etc., and indirect identifiers, i.e.,unique or rare combinations of attribute values in a dataset that can beused to re-identify individuals such as, for example, a 5-digit zipcode, gender, and date of birth. Personal data that have been“sufficiently anonymized”, following legal requirements, can be used forsupporting secondary purposes, such as various types of analyses. Forexample, anonymized data can be used by companies to support datamonetization initiatives by selling data collections for analyticalpurposes. Depending on insights that are to remain discoverable whenmining the anonymized data, and types of analyses that are to besupported, different data collections can be constructed and madeavailable to an end user.

SUMMARY

In a first aspect of an embodiment of the present invention, a method isprovided in a data processing system. The data processing systemincludes at least one processor and at least one memory. The at leastone memory includes instructions executed by the at least one processorto cause the at least one processor to implement a datasetde-identifying system. According to the method, a first set ofconstraints is determined based on anonymity requirements from a privacymodel. A second set of constraints is determined based on knowledgehiding requirements. A model is generated to determine minimum loss ofanalytic utility in the dataset for de-identification, while satisfyingthe first set of constraints and the second set of constraints. Themodel is applied to the dataset to determine changes to the dataset forde-identification that satisfy the first set of constraints and thesecond set of constraints. De-identified data is produced by modifyingthe dataset in accordance with the determined changes.

In a second aspect of an embodiment of the present invention, a systemfor producing de-identified data from a dataset is provided. The systemincludes at least one processor and at least one memory connected withthe at least one processor. The at least one processor is configured toperform: determining a first set of constraints based on anonymityrequirements from a privacy model; determining a second set ofconstraints based on knowledge hiding requirements; generating a modelto determine minimum loss of analytic utility in the dataset forde-identification while satisfying the first set of constraints and thesecond set of constraints; applying the model to the dataset todetermine changes to the dataset for de-identification that satisfy thefirst set of constraints and the second set of constraints; andproducing the de-identified data by modifying the dataset in accordancewith the determined changes.

In a third aspect of an embodiment of the present invention, a computerprogram product is provided. The computer program product includes atleast one computer readable storage medium having computer readableprogram code embodied therewith for execution on at least one processor.The computer readable program code is configured to be executed by theat least one processor to perform: determining a first set ofconstraints based on anonymity requirements from a data privacy model;determining a second set of constraints based on knowledge hidingrequirements; generating a model to determine minimum loss of analyticutility in a dataset for de-identification while satisfying the firstset of constraints and the second set of constraints; applying the modelto the dataset to determine changes to the dataset for de-identificationthat satisfy the first set of constraints and the second set ofconstraints; and producing de-identified data by modifying the datasetin accordance with the determined changes.

BRIEF DESCRIPTION OF THE DRAWINGS

Generally, like reference numerals in the various figures are utilizedto designate like components.

FIG. 1 illustrates an example environment in which embodiments of theinvention may operate.

FIG. 2 is a functional block diagram of a computing system that mayimplement one or more computing devices in various embodiments of theinvention.

FIG. 3 is a flowchart of an example process that may be performed invarious embodiments of the invention.

DETAILED DESCRIPTION

Anonymity requirements define what information in a dataset is to beprotected in order for the data set to be considered sufficientlyanonymized. Knowledge hiding requirements define what knowledge in thedataset is considered to be sensitive and thereby should be concealedor, equivalently, what knowledge is to be retained in the dataset afterprotecting the information that is to be protected. An original datasetmay be modified to produce a modified (also known as “sanitized”)dataset that satisfies all of the anonymity requirements and knowledgehiding requirements that do not conflict with the anonymityrequirements. In this way, the modified dataset may be used to controlthe level of knowledge that is available to users of the modifieddataset. In some embodiments, when a conflict exists between theknowledge hiding requirements and one or more anonymity requirements,the one or more anonymity requirements may be relaxed in order toeliminate the conflict.

Given an input dataset that includes personal and/or sensitiveinformation, anonymity requirements and sensitive knowledge hidingrequirements, embodiments of the invention produce a new dataset byminimally distorting the input dataset such that all anonymityconstraints, based on the anonymity requirements, and allnon-conflicting knowledge hiding constraints, which are based on thesensitive knowledge hiding requirements, are supported in the newdataset. Anonymity requirements may be based on a privacy model, whichmay include, but not be limited to any one of k-anonymity, completek-anonymity, 1-diversity, k_(m)-anonymity and set-based anonymization.Knowledge hiding requirements may be based on hiding sensitive patternsthat could be used to infer sensitive knowledge from the dataset, suchas knowledge that could disclose trade secrets and give a competitiveadvantage to a data holder.

FIG. 1 illustrates an example environment 100 in which variousembodiments may operate. Example environment 100 includes a network 102to which are connected a computing device 104 and a database 106.Computing device 104 and database 106 may be connected to network 102via a wired or a wireless connection.

Computing device 104 may include, but not be limited to, a mainframecomputer, a laptop personal computer or a desktop personal computer.Database 106 may include an original dataset to be minimally modifiedbased on anonymity requirements and knowledge hiding requirements.

Network 102 may be implemented by any number of any suitablecommunications media (e.g., wide area network (WAN), local area network(LAN), Internet, Intranet, etc.) or a combination of any of the suitablecommunications media. Network 102 may include wired and/or wirelessnetworks.

Although FIG. 1 shows computing device 104 and database 106 being remotefrom each other and connected via network 102, computing device 104 anddatabase 106 may be directly connected without a network in otheroperating environments.

FIG. 2 is a functional block diagram of a computing system 200 that mayimplement computing device 104 in various embodiments of the invention.Computing system 200 is shown in a form of a general-purpose computingdevice. Components of computing system 200 may include, but are notlimited to, one or more processors or processing units 216, a systemmemory 228, and a bus 218 that couples various system componentsincluding system memory 228 to one or more processing units 216.

Bus 218 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computing system 200 typically includes a variety of computer systemreadable media. Such media may be any available media that is accessibleby computing system 200, and may include both volatile and non-volatilemedia, removable and non-removable media.

System memory 228 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 230 and/or cachememory 232. Computing system 200 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 234 can be provided forreading from and writing to a non-removable, non-volatile magneticmedium (not shown, which may include a “hard drive” or a Secure Digital(SD) card). Although not shown, a magnetic disk drive for reading fromand writing to a removable, non-volatile magnetic disk (e.g., a “floppydisk”), and an optical disk drive for reading from or writing to aremovable, non-volatile optical disk such as a CD-ROM, DVD-ROM or otheroptical media can be provided. In such instances, each can be connectedto bus 218 by one or more data media interfaces. As will be furtherdepicted and described below, memory 228 may include at least oneprogram product having a set (e.g., at least one) of program modulesthat are configured to carry out the functions of embodiments of theinvention.

Program/utility 240, having a set (at least one) of program modules 242,may be stored in memory 228 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, the oneor more application programs, the other program modules, and the programdata or some combination thereof, may include an implementation of anetworking environment. Program modules 242 generally carry out thefunctions and/or methodologies of embodiments of the invention asdescribed herein.

Computing system 200 may also communicate with one or more externaldevices 214 such as a keyboard, a pointing device, one or more displays224, one or more devices that enable a user to interact with computingsystem 200, and/or any devices (e.g., network card, modem, etc.) thatenable computing system 200 to communicate with one or more othercomputing devices. Such communication can occur via Input/Output (I/O)interfaces 222. Still yet, computing system 200 can communicate with oneor more networks such as a local area network (LAN), a general wide areanetwork (WAN), and/or a public network (e.g., the Internet) via networkadapter 220. As depicted, network adapter 220 communicates with theother components of computing system 200 via bus 218. It should beunderstood that, although not shown, other hardware and/or softwarecomponents could be used in conjunction with computing system 200.Examples, include, but are not limited to: microcode, device drivers,redundant processing units, external disk drive arrays, RAID systems,tape drives, and data archival storage systems, etc.

FIG. 3 is a flowchart that illustrates an example process that may beperformed in various embodiments. The process may begin with computingdevice 104 determining a first set of constraints based on anonymityrequirements from a privacy model (act 302). This may be best explainedby reference to an example.

In the following example, the considered privacy model is completek-anonymity, where k=2. Consider a transaction dataset, where each rowcorresponds to a unique individual, and carries a record ID. For eachindividual represented in the dataset, a set of diagnoses codes thatthis individual has been associated with during a hospital visit, isprovided. All data values are fictional. The complete k-anonymity modelrequires that each set of diagnosis codes (also known as “items”) thatappears in the record of one individual, must also appear in the recordsof K−1 other individuals in the dataset. Table 1 shows an originaldataset having one or more diagnoses codes for each record. The originaldataset, in this example, has eight records as shown in Table 1.

TABLE 1 Original Dataset ID Diagnoses Codes 1 295.00 295.01 2 205.00295.01 296.01 3 295.01 296.01 4 296.00 5 295.00 296.00 295.01 296.01 6296.01 7 295.01 8 295.00 296.00

According to the complete k-anonymity privacy model, where k=2, oneanonymity constraint is specified for each data record ID as follows:

-   ID=1: support({1295.00 295.01)})≥2 OR support ({1295.00 295.011})=0-   ID=2: support({205.00 295.01 296.01)}≥2 OR support({205.00    295.01296.01})=0-   ID=3: support({295.01 296.01})≥2 OR support({1295.01 296.011})=0-   ID=4: support({296.00})≥2 OR support({296.00})=0-   ID=5: support({1295.00 296.00 295.01 296.01})≥2 OR support({295.00    296.00 295.01296.0})=0-   ID=6: support({296.01})≥2 OR support({296.01})=0-   ID=7: support({295.01})≥2 OR support({295.01})=0-   ID=8: support({1295.00 296.00})≥2 OR support({1295.00 296.00})=0    where the support (or support count) of a set of items (or itemset)    is a number of records in the dataset that contain the corresponding    itemset.

Next, one or more knowledge hiding constraints may be determined basedon patterns to be concealed (act 304). Typically, each pattern to beconcealed corresponds to one sensitive knowledge hiding constraint.Knowledge hiding requirements may be determined by data mining or dataanalysis, which may reveal patterns. Such patterns may be used todiscover sensitive data in a de-identified dataset. In the above exampleof Table 1, a frequent itemset pattern of {295.00 296.00} is consideredto be sensitive and a minimum frequency threshold of 0.2 may bespecified. That is, no more than 20% of the record IDs are to containthe sensitive pattern {295.00 296.00} in the sanitized dataset. This canbe specified as support({295.00 296.00})<(0.2×8 rows)<1.6 records. Sincea number of records should be expressed as an integer, the knowledgehiding constraint should be expressed as support({295.00 296.00})≤1.

The approach used in this example includes protection of anonymity andknowledge hiding using item suppression. Therefore, each record in aresulting de-identified dataset will be associated with a same number ofitems or fewer items than in the original dataset. Keeping this in mind,an item that is associated with a record, or an individual, in Table 1may either continue to be associated with the individual or record, ormay be deleted (suppressed) from the record. Each item from a record inthe original dataset may be replaced with a binary variable as shown inTable 2, where each original item is replaced with a unique binaryvariable u_(ij), in which i and j, respectively, correspond to a row andcolumn of Table 2. Each binary variable has a value of 1 or 0, where 1indicates that a corresponding original value remains in the dataset and0 indicates that the corresponding original value is to be suppressed inthe dataset. If each binary variable is replaced with a “1”, a result isthe original dataset.

TABLE 2 Intermediate Data Representation ID 295.00 296.00 295.01 296.011 u₁₁ 0 u₁₃ 0 2 u₂₁ 0 u₂₃ u₂₄ 3 0 0 u₃₃ u₃₄ 4 0 u₄₂ 0 0 5 u₅₁ u₅₂ u₅₃u₅₄ 6 0 0 0 u₆₄ 7 0 0 u₇₃ 0 8 u₈₁ u₈₁ 0 0

A constraint satisfaction problem with a first set of anonymityconstraints and a second set of knowledge hiding constraints, may becreated (act 306). Rewriting the anonymity and knowledge hidingconstraints using the binary variables, results in:

-   ID=1: u₁₁u₁₃+u₂₁u₂₃+u₅₁u₅₃≥2 OR u₁₁u₁₃+u₂₁u₂₃+u₅₁u₅₃=0-   ID=2: u₂₁u₂₃u₂₄+u₅₁u₅₃u₅₄≥2 OR u₂₁u₂₃u₂₄+u₅₁u₅₃u₅₄=0-   ID=3: u₂₃u₂₄+u₃₃u₃₄ u₅₃u₅₄≥2 OR u₂₃u₂₄+u₃₃u₃₄+u₅₃u₅₄=0-   ID=4: u₄₂+u₅₂+u₈₂≥2 OR u₄₂+u₅₂+u₈₂=0-   ID=5: u₅₁u₅₂u₅₃u₅₄≥2 OR u₅₁u₅₂u₅₃u₅₄=0-   ID=6: u₂₄+u₃₄+u₅₄+u₆₄≥2 OR u₂₄+u₃₄+u₅₄+u₆₄=0-   ID=7: u₁₃+u₂₃+u₃₃+u₅₃+u₇₃≥2 OR u₁₃+u₂₃+u₃₃+u₅₃+u₇₃=0-   ID=8: u₅₁u₅₂+u₈₁u₈₂≥2 OR u₅₁u₅₂+u₈₁u₈₂=0-   Knowledge Hiding Constraint: u₅₁u₅₂+u₈₁u₈₂≤1

For a record to support an itemset, the record must contain all items ofthe itemset. A product of 1 for corresponding variables indicates thatall items of the itemset are present in the record.

The objective function may be set to minimize information loss in thedataset (act 308). Thus, in this example, the constraint satisfactionproblem (CSP) to be solved becomes:maximize(u ₁₁ +u ₁₃ +u ₂₁ +u ₂₃ +u ₂₄ +u ₃₃ +u ₃₄ +u ₄₂ +u ₅₁ +u ₅₂ +u₅₃ +u ₅₄ +u ₆₄ +u ₇₃ +u ₈₁ +u ₈₂)

-   -   subject to

-   u₁₁u₁₃+u₂₁u₂₃+u₅₁u₅₃≥2 OR u₁₁u₁₃+u₂₁u₂₃+u₅₁u₅₃=0

-   u₂₁u₂₃u₂₄+u₅₁u₅₃u₅₄≥2 OR u₂₁u₂₃u₂₄+u₅₁u₅₃u₅₄=0

-   u₂₃u₂₄+u₃₃u₃₄+u₅₃u₅₄≥2 OR u₂₃u₂₄+u₃₃u₃₄+u₅₃u₅₄=0

-   u₄₂+u₅₂+u₈₂≥2 OR u₄₂+u₅₂+u₈₂=0

-   u₅₁u₅₂u₅₃u₅₄≥2 OR u₅₁u₅₂u₅₃u₅₄=0

-   u₂₄+u₃₄+u₅₄+u₆₄≥2 OR u₂₄+u₃₄+u₅₄+u₆₄=0

-   u₁₃+u₂₃+u₃₃+u₅₃+u₇₃≥2 OR u₁₃+u₂₃+u₃₃+u₅₃+u₇₃=0

-   u₅₁u₅₂+u₈₁u₈₂≥2 OR u₅₁u₅₂+u₈₁u₈₂=0

-   u₅₁u₅₂+u₈₁u₈₂≤1

-   u_(ij)∈{0, 1}, ∀i, j

Next, an attempt is made to solve the CSP to determine changes to thedataset in order to satisfy the anonymity and knowledge hidingconstraints (act 310). A solution to the CSP, in this example, involvesassigning a binary value of 0 or 1 to each of the binary variablesu_(ij). A last step to solving the CSP involves replacing the binaryvariables in an intermediate form of the data set, shown in Table 2,with corresponding values of 0 or 1 obtained via the solution of the CSPas shown in Table 3. The zero values in bold correspond to removed itemsfrom the original dataset. Only two values have been removed to solvethe CSP in this example.

TABLE 3 Solution of the CSP ID 295.00 296.00 295.01 296.01 1 1 0 1 0 2 10 1 1 3 0 0 1 1 4 0 1 0 0 5 1 0 1 1 6 0 0 0 1 7 0 0 1 0 8 0 1 0 0

A solution to the above-mentioned CSP is found by using integerprogramming techniques, which are well known. For the above example, theset of constraints for the anonymity requirements involves a specialcase having a disjunction with an inequality and an equation. Constraintsatisfaction problems involving generalized linear constraints thatinclude disjunctions with equations and inequalities have been studied.

According to FIG. 3, various embodiments may determine whether the CSPis solved (act 312) by determining whether a possible solution satisfiesthe anonymity constraints, the knowledge hiding constraints and theobjective function regarding minimizing information loss in the dataset.If the CSP is determined to be solved, then de-identified data isproduced (act 316), as shown in Table 4 for this example, in which thevalue (item) 296.00 is suppressed in record ID 5 and the value (item)295.00 is suppressed in record ID 8.

TABLE 4 De-identified Dataset ID Diagnoses Codes 1 295.00 295.01 2205.00 295.01 296.01 3 295.01 296.01 4 296.00 5 295.00 

 295.01 296.01 6 296.01 7 295.01 8

 296.00

In cases in which multiple solutions having a same distance, orobjective criterion, exist, one solution may be selected at random.Alternatively, a second selection criterion may be used to decide whichsolution to select.

In cases in which a solution for the CSP cannot be determined due to theanonymity constraints conflicting with the knowledge hiding constraints,then a conflicting knowledge hiding constraint may be relaxed, orremoved, from the CSP (act 314) and another attempt is made to solve theCSP (act 310). It should be noted that the anonymity constraints alonewill always—by construction—be solvable. Therefore, a produced CSP willalways be able to provide a required anonymity level, regardless of anumber of sensitive knowledge patterns that will be hidden.

Although the above example uses the complete k-anonymity privacy modelwith minimum data distortion, other privacy models may also be usedincluding, but not limited to, 1-diversity, k^(m)-anonymity andset-based anonymization. As previously mentioned, in the above example,all items involved in anonymity and knowledge hiding constraints werereplaced with binary variables. However, in other cases, fewer originalitems may be replaced with binary variables.

Different approaches for solving a CSP may be used depending on a typeof constraints that are produced in the CSP due to a type of privacymodel to be enforced and a type of sensitive knowledge patterns to beconcealed. Further, different strategies for relaxing constraints forunsolvable CSPs may be used in order to determine a solution. In someembodiments, machine learning may be employed to relax or modify theknowledge hiding constraints.

In the above example, the optimization criterion involved minimizingdata distortion, which was captured as a distance between an originaldata set and its de-identified counterpart. In other embodiments, otheroptimization criteria may be used instead of distance in order to ensurethat a produced solution maintains high data utility. For example, insome embodiments, a sanitized dataset may be used for supporting aparticular analytic task. In this situation, data utility is to bemaintained such that the modified dataset supports this particularanalytic task. Accordingly, an optimization criterion can be specifiedsuch that a solution of the CSP maximizes utility of the modifieddataset for supporting the analytic task.

Due to a complexity of solving very large CSPs, which contain manyconstraints and variables, heuristic techniques and techniques that arebased on structural CSP decomposition and parallel solving may also beused to reduce data operations and provide faster de-identification.

It will be appreciated that the embodiments described above andillustrated in the drawings represent only a few of the many ways ofimplementing various embodiments.

The environment of the present invention embodiments may include anynumber of computer or other processing systems and databases or otherrepositories arranged in any desired fashion, where the presentinvention embodiments may be applied to any desired type of computingenvironment (e.g., cloud computing, client-server, network computing,mainframe, stand-alone systems, etc.). The computer or other processingsystems employed by the present invention embodiments may be implementedby any number of any personal or other type of computer or processingsystem (e.g., desktop, laptop, PDA, mobile devices, etc.), and mayinclude any commercially available operating system and any combinationof commercially available and custom software (e.g., browser software,communications software, server software). These systems may include anytypes of monitors and input devices (e.g., keyboard, mouse, voicerecognition, etc.) to enter and/or view information.

It is to be understood that the software of the present inventionembodiments may be implemented in any desired computer language andcould be developed by one of ordinary skill in the computer arts basedon the functional descriptions contained in the specification andflowcharts illustrated in the drawings. Further, any references hereinof software performing various functions generally refer to computersystems or processors performing those functions under software control.The computer systems of the present invention embodiments mayalternatively be implemented by any type of hardware and/or otherprocessing circuitry.

The various functions of the computer or other processing systems may bedistributed in any manner among any number of software and/or hardwaremodules or units, processing or computer systems and/or circuitry, wherethe computer or processing systems may be disposed locally or remotelyof each other and communicate via any suitable communications medium(e.g., LAN, WAN, Intranet, Internet, hardwire, modem connection,wireless, etc.). For example, the functions of the present inventionembodiments may be distributed in any manner among the various computingsystems, and/or any other intermediary processing devices. The softwareand/or algorithms described above and illustrated in the flowcharts maybe modified in any manner that accomplishes the functions describedherein. In addition, the functions in the flowcharts or description maybe performed in any order that accomplishes a desired operation.

The software of the present invention embodiments may be available on anon-transitory computer useable medium (e.g., magnetic or opticalmediums, magneto-optic mediums, floppy diskettes, CD-ROM, DVD, memorydevices, etc.) of a stationary or portable program product apparatus ordevice for use with stand-alone systems or systems connected by anetwork or other communications medium.

The communication network may be implemented by any number of any typeof communications network (e.g., LAN, WAN, Internet, Intranet, VPN,etc.). The computer or other processing systems of the present inventionembodiments may include any conventional or other communications devicesto communicate over the network via any conventional or other protocols.The computer or other processing systems may utilize any type ofconnection (e.g., wired, wireless, etc.) for access to the network.Local communication media may be implemented by any suitablecommunication media (e.g., local area network (LAN), hardwire, wirelesslink, Intranet, etc.).

The system may employ any number of any conventional or other databases,data stores or storage structures (e.g., files, databases, datastructures, data or other repositories, etc.) to store information. Thedatabase system may be implemented by any number of any conventional orother databases, data stores or storage structures to store information.The database system may be included within or coupled to server and/orclient systems. The database systems and/or storage structures may beremote from or local to a computer or other processing systems, and maystore any desired data.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”,“comprising”, “includes”, “including”, “has”, “have”, “having”, “with”and the like, when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiments were chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as, for example, radio waves orother freely propagating electromagnetic waves, electromagnetic wavespropagating through a waveguide or other transmission media (e.g., lightpulses passing through a fiber-optic cable), or electrical signalstransmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figs. illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figs. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The invention claimed is:
 1. A system for producing de-identified datafrom a dataset, the system comprising: at least one processor; and atleast one memory connected with the at least one processor, wherein theat least one processor is configured to perform: determining a first setof constraints based on anonymity requirements from a privacy model,each record of the dataset having an anonymity constraint of the firstset of constraints; determining a second set of constraints based onknowledge hiding requirements, each constraint of the second set ofconstraints corresponding to a respective pattern to be concealed in thede-identified data; generating a model to determine minimum loss ofanalytic utility in the dataset for de-identification while satisfyingthe first set of constraints and the second set of constraints, thegenerating the model further comprising: selectively replacing items ineach constraint of the first set of constraints and items in eachconstraint of the second set of constraints with binary variables usedby a constraint satisfaction problem, wherein each of the binaryvariables indicates either a presence or an absence of a correspondingitem in the de-identified data, and assigning a value to each of thebinary variables such that the first set of constraints and the secondset of constraints are satisfied and a sum of all of the binaryvariables of the constraint satisfaction problem is maximized; applyingthe model to the dataset to determine changes to the dataset forde-identification that satisfy the first set of constraints and thesecond set of constraints, the applying the model further comprising:formulating and solving the constraint satisfaction problem, anddetermining ones of items of the dataset to be changed based on valuesof the binary variables of the constraint satisfaction problem; andproducing the de-identified data by modifying the dataset in accordancewith the determined changes.
 2. The system of claim 1, wherein theproducing the de-identified data by modifying the dataset in accordancewith the determined changes further comprises: suppressing thedetermined ones of the items of the dataset to produce the de-identifieddata.
 3. The system of claim 1, wherein the at least one processor isfurther configured to perform: identifying one or more conflicts in themodel between the first set of constraints and the second set ofconstraints; and relaxing one or more constraints of the second set ofconstraints in the model to determine the changes to the dataset.
 4. Thesystem of claim 1, wherein the privacy model includes one from a groupconsisting of k-anonymity, complete k-anonymity, l-diversity,km-anonymity and set-based anonymization.
 5. A computer program productcomprising at least one computer readable storage medium having computerreadable program code embodied therewith for execution on at least oneprocessor, the computer readable program code being configured to beexecuted by the at least one processor to perform: determining a firstset of constraints based on anonymity requirements from a privacy model,each record of a dataset having an anonymity constraint of the first setof constraints; determining a second set of constraints based onknowledge hiding requirements, each constraint of the second set ofconstraints corresponds to a respective pattern to be concealed inde-identified data; generating a model to determine minimum loss ofanalytic utility in a dataset for de-identification while satisfying thefirst set of constraints and the second set of constraints, thegenerating the model further comprising: selectively replacing items ineach constraint of the first set of constraints and items in eachconstraint of the second set of constraints with binary variables usedby the constraint satisfaction problem, wherein each of the binaryvariables indicates either a presence or an absence of a correspondingitem in the de-identified data, and assigning a value to each of thebinary variables such that the first set of constraints and the secondset of constraints are satisfied and a sum of the binary variables ofthe constraint satisfaction problem is maximized; applying the model tothe dataset to determine changes to the dataset for de-identificationthat satisfy the first set of constraints and the second set ofconstraints, the applying the model comprising: formulating and solvingthe constraint satisfaction problem, and determining ones of items ofthe dataset to be changed based on values of the binary variables of theconstraint satisfaction problem; and producing the de-identified data bymodifying the dataset in accordance with the determined changes.
 6. Thecomputer program product of claim 5, wherein the computer readableprogram code is configured to be executed by the at least one processorto perform: identifying one or more conflicts in the model between thefirst set of constraints and the second set of constraints; and relaxingone or more constraints of the second set of constraints in the model todetermine the changes to the dataset.